Novel Modelling Strategies for High-frequency Stock Trading Data
- URL: http://arxiv.org/abs/2212.00148v1
- Date: Wed, 30 Nov 2022 22:50:11 GMT
- Title: Novel Modelling Strategies for High-frequency Stock Trading Data
- Authors: Xuekui Zhang, Yuying Huang, Ke Xu and Li Xing
- Abstract summary: We propose three novel modelling strategies for processing raw data.
We show how our strategies often lead to statistically significant improvement in predictions.
The three strategies improve the F1 scores of the SVM models by 0.056, 0.087, and 0.016, respectively.
- Score: 4.639889477442706
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Full electronic automation in stock exchanges has recently become popular,
generating high-frequency intraday data and motivating the development of near
real-time price forecasting methods. Machine learning algorithms are widely
applied to mid-price stock predictions. Processing raw data as inputs for
prediction models (e.g., data thinning and feature engineering) can primarily
affect the performance of the prediction methods. However, researchers rarely
discuss this topic. This motivated us to propose three novel modelling
strategies for processing raw data. We illustrate how our novel modelling
strategies improve forecasting performance by analyzing high-frequency data of
the Dow Jones 30 component stocks. In these experiments, our strategies often
lead to statistically significant improvement in predictions. The three
strategies improve the F1 scores of the SVM models by 0.056, 0.087, and 0.016,
respectively.
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